Publication

Abstract : Background Edible flowers have long been valued across cultures for their vibrant appearance and culinary versatility. Recent studies have highlighted their rich nutrient and phytochemical profiles, positioning them as promising functional food ingredients. Scope and approach This review synthesizes literature from 2018 to 2025 on the nutritional, phytochemical, and health-promoting attributes of edible flowers. It presents analytical comparisons between conventional techniques (HPLC, GC-MS, NMR) and AI-enhanced methods (machine learning based pattern recognition, and spectral data mining) to evaluate their efficacy in compound identification and bioactivity prediction. Key findings and conclusions Major edible flowers contain high levels of flavonoids, polyphenols, anthocyanins, vitamins, and minerals, with compositional variability influenced by genotype, environment, and developmental stage. AI-based QSAR modeling and spectral data mining have improved the detection of low-abundance bioactive and supported mechanistic insights. Their applications span bakery products, beverages, and nutraceutical formulations, leveraging antioxidant, anti-inflammatory, cardiovascular, neuroprotective, and anticancer properties. Culinary use at typical dosages shows minimal toxicity or allergenicity. Integrating omics technologies with AI data fusion is recommended to standardize phytochemical profiling, enhance model interpretability, and accelerate translation into validated functional foods and nutraceutical products.